Bilevel optimization has become a powerful tool in a wide variety of mac...
Reinforcement learning often needs to deal with the exponential growth o...
Partially observable Markov decision processes (POMDPs) have been widely...
We study model-free reinforcement learning (RL) algorithms in episodic
n...
Continual learning (CL), which aims to learn a sequence of tasks, has
at...
In many applications of Reinforcement Learning (RL), it is critically
im...
Switching costs, which capture the costs for changing policies, are rega...
We study the constrained reinforcement learning problem, in which an age...
In this paper, we study the problem of regret minimization in reinforcem...
In this paper, we consider the Gaussian process (GP) bandit optimization...
In this paper, we consider the time-varying Bayesian optimization proble...
The demand for seamless Internet access under extreme user mobility, suc...
We study a variant of the classical multi-armed bandit problem (MABP) wh...
In this paper, we propose a new privacy-preserving, automated contact tr...
We investigate contextual bandits in the presence of side-observations a...
In this note, we apply Stein's method to analyze the performance of gene...
In this note, we apply Stein's method to analyze the steady-state
distri...
We consider the load balancing problem in large-scale heterogeneous syst...
The emergence of bandwidth-intensive latency-critical traffic in 5G Netw...
In this paper, we prove that under mild stochastic assumptions,
work-con...
In this paper, we prove that under mild stochastic assumptions,
work-con...
Stochastic multi-armed bandits form a class of online learning problems ...
One fundamental challenge in 5G URLLC is how to optimize massive MIMO
co...
We study the K-item knapsack problem (, 1.5-dimensional KP), which is
a ...
We study the K-item knapsack problem (, 1.5-dimensional knapsack
problem...
It has been established that when the gradient coding problem is distrib...
This paper studies the problem of identifying any k distinct arms among ...
In this paper, we consider a load balancing system under a general pull-...
Heavy traffic analysis for load balancing policies has relied heavily on...
We study multi-armed bandit problems with graph feedback, in which the
d...
In large scale distributed linear transform problems, coded computation ...
We design new algorithms for maximizing a monotone non-negative submodul...
In this work, we address the open problem of finding low-complexity
near...
Recent works show that power-proportional data centers can save energy c...
In a large-scale and distributed matrix multiplication problem
C=A^B, wh...
The multi-armed bandit problem has been extensively studied under the
st...
We consider stochastic multi-armed bandit problems with graph feedback, ...
We establish a unified analytical framework for load balancing systems, ...
Caching systems using the Least Recently Used (LRU) principle have now b...